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<h1>R projects, version control, publishing and replicability</h1>

<h2>R projects</h2>

<p>RStudio projects allows to divide your work into multiple contexts (i.e. projects).<br/>
Each project has its own working directory, workspace, history, and source documents.</p>

<p>For more details, the official <a href="http://www.rstudio.com/ide/docs/using/projects">R projects</a> page.</p>

<h2>Version control</h2>

<p>Once you&#39;ve set up an R projects, RStudio make it very easy to sync it with a git repositery</p>

<p>See <a href="Using%20Version%20Control%20with%20RStudio">version control with RStudio</a> for more details on that.</p>

<h2>Publishing</h2>

<p>knitR is a package which allows you to publish your workflow extremely easily (similar to ipython notebook). <strong>Publish</strong> does not mean for a journal publication (even if it could), it&#39;s more like a huge improvement on commenting your code. If you run your analysis like that, it will be very easy and straighforward to go back to these analysis in 6 months or 2 years (and sometimes you will need to do that).</p>

<h2>Exercise</h2>

<h3>R project</h3>

<p>Create a new Rproject.<br/>
Generate a random set of data (as Ulf taught you before).<br/>
Make any analysis you want on it.<br/>
Close everything, and then try to get back and find everything as you left them before.</p>

<h3>knitR</h3>

<p>Make a short &ldquo;how to&rdquo; in R, and publish it as an html page. Some suggestions on topics:</p>

<ul>
<li>how to make a t-test</li>
<li>how to use aggregate() on a data.frame</li>
<li>how to </li>
</ul>

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